Visual Flight Rules Stabilised Approach: Identifying Human-Factor Influences on Incidents and Accidents During Stabilised Approach, Landing, and Go-Around Flight Phases for General Aviation
Bibliographic record
Abstract
According to the Transportation Safety Board of Canada, between 2013 and 2023, 62% of aviation accidents occurred during the approach, landing, and post-impact phases of flight. Hence, this study targets factors contributing to increased accident rates during the final stages of flight. It will review how pilot experience influences decision-making and identifies mitigation strategies, focusing on go-arounds to prevent accidents during these critical phases. Surveys and roundtable discussions were conducted to identify factors influencing pilot performance during approach, landing, and go-around manoeuvres. By using a mixed-methods approach that combined thematic and statistical analyses, key safety factors were identified, including situational awareness, decision-making, and operational complexity. The study also examined the relationship between experience and decision-making, highlighting areas for targeted interventions to improve safety. The research emphasises the importance of integrating decision-making considerations into training programmes and connecting these to human factors. Through identifying areas for improvement, this study offers a safety-driven framework to address decision-making challenges during approach, landing, and go-around phases, with the objective of reducing accident and incident rates in general aviation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".